System and method for optimizing flow analysis in static code analysis using machine learning

Abstract

System and method for optimizing flow analysis for detections of code violations in a computer program, using machine learning include: analyzing the computer program for code violations; identifying functions or methods with execution path to code violations and creating a first dataset for suspicious functions or methods; identifying functions or methods with no execution path to code violations and creating a second dataset for unsuspicious functions or method; training a machine learning model to classify the suspicious and unsuspicious functions or method using the first and second datasets, wherein the trained model outputs a probability score for the methods or functions with execution paths to code violations; and utilizing the machine learning model to analyze code violations in a new computer program responsive to the probability scores for the methods or functions with execution paths to code violations.

1 . A method for optimizing flow analysis for detections of code violations in a computer program, using machine learning, the method comprising:
 analyzing the computer program for code violations;   identifying functions or methods with execution path to code violations and creating a first dataset for suspicious functions or methods;   identifying functions or methods with no execution path to code violations and creating a second dataset for unsuspicious functions or method;   training a machine learning model to classify the suspicious and unsuspicious functions or method using the first and second datasets, wherein the trained model outputs a probability score for the methods or functions with execution paths to code violations; and   utilizing the machine learning model to analyze code violations in a new computer program responsive to the probability scores for the methods or functions with execution paths to code violations.   
     
     
         2 . The method of  claim 1 , wherein said utilizing the machine learning model to analyze code violations further comprises executing a flow analysis tool. 
     
     
         3 . The method of  claim 1 , further comprising selecting a predetermined number of execution paths to code violations with highest probability scores for correcting the code violations. 
     
     
         4 . The method of  claim 1 , further comprising ranking the functions or methods based on their probability scores. 
     
     
         5 . The method of  claim 1 , wherein the code violations include security risks. 
     
     
         6 . The method of  claim 1 , wherein said training the machine learning model comprises vectorizing the first and second datasets. 
     
     
         7 . A system for optimizing flow analysis for detections of code violations in a computer program, using machine learning comprising:
 a flow analysis engine for analyzing the computer program for code violations;   means for identifying functions or methods with execution path to code violations and creating a first dataset for suspicious functions or methods;   means for identifying functions or methods with no execution path to code violations and creating a second dataset for unsuspicious functions or method;   means for training a machine learning model to classify the suspicious and unsuspicious functions or method using the first and second datasets, wherein the trained model outputs a probability score for the methods or functions with execution paths to code violations; and   means for utilizing the machine learning model to analyze code violations in a new computer program responsive to the probability scores for the methods or functions with execution paths to code violations.   
     
     
         8 . The system of  claim 7 , wherein said utilizing the machine learning model to analyze code violations further comprises executing a flow analysis tool. 
     
     
         9 . The system of  claim 7 , further comprising selecting a predetermined number of execution paths to code violations with highest probability scores for correcting the code violations. 
     
     
         10 . The system of  claim 7 , further comprising ranking the functions or methods based on their probability scores. 
     
     
         11 . The system of  claim 7 , wherein the code violations include security risks. 
     
     
         12 . The system of  claim 7 , wherein said training the machine learning model comprises vectorizing the first and second datasets. 
     
     
         13 . A tangible storage medium for storing a plurality of computer codes, the plurality of computer codes when executed by one more computers performing a method for prioritizing code violations in a computer program, using machine learning, the method comprising:
 analyzing the computer program for code violations;   identifying functions or methods with execution path to code violations and creating a first dataset for suspicious functions or methods;   identifying functions or methods with no execution path to code violations and creating a second dataset for unsuspicious functions or method;   training a machine learning model to classify the suspicious and unsuspicious functions or method using the first and second datasets, wherein the trained model outputs a probability score for the methods or functions with execution paths to code violations; and   utilizing the machine learning model to analyze code violations in a new computer program responsive to the probability scores for the methods or functions with execution paths to code violations.   
     
     
         14 . The tangible storage medium of  claim 13 , wherein said utilizing the machine learning model to analyze code violations further comprises executing a flow analysis tool. 
     
     
         15 . The tangible storage medium of  claim 13 , further comprising computer codes for selecting a predetermined number of execution paths to code violations with highest probability scores for correcting the code violations. 
     
     
         16 . The tangible storage medium of  claim 13 , further comprising computer codes for ranking the functions or methods based on their probability scores. 
     
     
         17 . The tangible storage medium of  claim 13 , wherein the code violations include security risks. 
     
     
         18 . The tangible storage medium of  claim 13 , wherein said training the machine learning model comprises vectorizing the first and second datasets.